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1 In Silico Screening of Zinc (II) Enzyme Inhibitors Using ILP Tadasuke Ito, Shotaro Togami, Shin Aoki and Hayato Ohwada Department of Industrial Administration.

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Presentation on theme: "1 In Silico Screening of Zinc (II) Enzyme Inhibitors Using ILP Tadasuke Ito, Shotaro Togami, Shin Aoki and Hayato Ohwada Department of Industrial Administration."— Presentation transcript:

1 1 In Silico Screening of Zinc (II) Enzyme Inhibitors Using ILP Tadasuke Ito, Shotaro Togami, Shin Aoki and Hayato Ohwada Department of Industrial Administration Tokyo University of Science

2 2 In-silico screening is a powerful, low-cost method of finding strong binders for proteins and enzymes ・ Structure-Based Virtual Screening (SBVS) Introduction 1/4 ・ Ligand-Based Virtual Screening (LBVS) ⇒ Docking Simulation ⇒ Machine Learning (FingerPrint, Chemical Descriptor, …)

3 3 Introduction 2/4 ・ Machine Learning ・ Inhibitor DataBase liganddecoy ・ Machine Learning Method Inhibitor candidates SVM, RandomForest, …ILP classification model Result

4 4 CAH2 contain zinc. CA inhibitors Introduction 3/4 Remedy ・ Epilepsy Catalytic reaction : Carbonic anhydrase II (CAH2) CAH2 ・ Glaucoma

5 5 Drug-discovery researchers expects Introduction 4/4 Our objective is screening many inhibitor candidates of CAH2 high classification performance for inhibitors clear classification model Classifier provides high classification performance graphical classification model

6 6 Data Extraction Method 1/4 Obtain ligands and decoys actives_final.mo2 ⇒ Ligand(inhibitor) decoys_final.mol2 ⇒ Decoy(non-inhibitor) Database of Useful Decoys: Enhanced (DUD-E) Number of CA inhibitors Database Training data

7 7 Machine learning with ILP Method 2/4 Clauses(Input) ・ bond(compound, atomid, atomid, bondtype) ・ atom(compound, atomid, atomtype) ・ ring(compound, ringid, atomid, ringsize) ILP system : GKS Input data : CompoundStructure actives_final.mo2 decoys_final.mo2 Extraction Rule(Output) bond(A, B, C, 2), atom(A, B, cl), ring(A, D, B, 6) Class Positive ⇒ Ligand(actives_final.mo2) Negative ⇒ Decoy(decoys_final.mo2)

8 8 Method 3/4 training data If the compound applies to rules, the predicted value is 1. If not, the predicted value is 0. ※ 1 : ligand, 0 : decoy test data bond(A, B, C, 2), atom(A, B, cl), ring(A, D, B, 6) applies to rules Compound 1 Compound 2 Compound 3 …. Compound n make rules ligand or decoy?

9 9 Evaluation method Method 4/4 Ligand : 14Decoy : 8 22 inhibitor candidates that are not included in DUD-E

10 10 Classification result Results 1/4 Data set training data : ligands = 492, decoys = 3000 test data : ligands = 14, decoys = 8 Parameters depth = 10, negative = 10, positive = 10, clause_size = 6 Output 11 rules

11 11 Results 2/4 dock(A) :- atom(A, B, s), bond(A, C, B, 1), bond(A, B, D, 2), dock(A) :- bond(A, C, E, 1), bond(A, E, F, 2), ring(A, G, F, 6) Rule 2 Score Training data Positive : 125 / 492 Negative : 8 / 3000 Test data Positive : 12 / 14 Negative : 2 / 8

12 12 Results 3/4 dock(A) :- bond(A, B, C, 1), atom(A, C, s), bond(A, D, B, 2), dock(A) :- bond(A, E, D, 1), bond(A, C, F, 2), ring(A, G, E, 5) Rule 1 Score Training data Positive : 118 / 492 Negative : 7 / 3000 Test data Positive : 1 / 14 Negative : 0 / 8

13 13 Results 4/4 dock(A) :- bond(A, B, C, 1), atom(A, B, s), atom(A, C, n), dock(A) :- bond(A, D, B, 1), bond(A, D, E, 2), ring(A, F, D, 6) Rule 4 Score Training data Positive : 191 / 492 Negative : 10 / 3000 Test data Positive : 1 / 14 Negative : 0 / 8 sulfonamide

14 14 Conclusion Machine Learning : Inductive Logic Programming (ILP) Database : Database of Useful Decoys: Enhanced (DUD-E) Target enzymes : Carbonic anhydrase II predicts ligand high performance Method provides a clear classification model Classified new inhibitor candidates (14 ligands, 8 decoys) Our method could be applied to other zinc enzymes. angiotensin-converting enzyme, histone deacetylase, metallo-B-lactamase, …

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